1 Source libraries, functions and plotting parameters

library(here)

source(here::here("code/scripts/20210628_source.R"))

2 Get full list of traits, associations and studies from the GWAS Catalog

2.1 Traits

NOTE: only run once.

date_of_collection = "20210809"
long_term_dir = "/nfs/research/birney/users/ian/hmn_fst/gwasrapidd"
out_file = here::here("code/snakemake/20210625/config",
                      paste(date_of_collection, "_all_traits.tsv", sep = ""))

# Get list of all traits in database
all_traits = gwasrapidd::get_traits()

# Filter out IDs that cause an error
all_traits_tbl = all_traits@traits %>% 
  dplyr::filter(efo_id != "CHEBI:7916") %>%
  # Save to file
  readr::write_tsv(out_file)
all_traits_tbl %>% 
  DT::datatable(.)

2.2 Associations

NOTE: only run once.

#Need to do them sequentially because the GWAS Catalog can't handle dozens (let alone thousands) of simultaneous queries.
date_of_collection = "20210809"
long_term_dir = "/nfs/research/birney/users/ian/hmn_fst/gwasrapidd"

# create output dir
out_dir = file.path(long_term_dir,
                    date_of_collection,
                    "associations_raw")
dir.create(out_dir, recursive = T)

# Get associations
counter = 0
lapply(all_traits_tbl$efo_id, function(EFO_ID) {
  counter <<- counter + 1
  # set output file name
  out_path = file.path(out_dir,
                       paste(EFO_ID, ".rds", sep = ""))
  # if output file doesn't already exist, get associations and save
  if (!file.exists(out_path)){
    out = gwasrapidd::get_associations(efo_id = EFO_ID)
    saveRDS(out, out_path)
  } else {
    print(paste(counter, ". ", EFO_ID, ": File already exists.", sep = ""))
  }
})

3 Run Snakemake pipeline to extract data

Full Snakemake pipeline here: https://github.com/brettellebi/human_traits_fst/tree/master/code/snakemake/20210625

4 Read in processed data

4.1 Specify target directory

target_dir = "/nfs/research/birney/users/ian/hmn_fst/gwasrapidd/20210809"

4.2 Total initial counts

assocs_path = file.path(target_dir, "associations_raw")

assocs_files = list.files(assocs_path, full.names = T)

# Read in associations
assocs_raw = lapply(assocs_files, function(EFO_ID){
  readRDS(EFO_ID)
})

names(assocs_raw) = basename(assocs_files) %>% 
  stringr::str_remove(".rds")

# Get counts
raw_counts = purrr::map(assocs_raw, function(EFO_ID){
  EFO_ID@risk_alleles %>%
    dplyr::pull(variant_id) %>% 
    unique(.) %>% 
    length(.) %>% 
    data.frame("N" = .)
}) %>% 
  dplyr::bind_rows(.id = "EFO_ID") %>% 
  # Add `trait`
  dplyr::left_join(all_traits_tbl %>% 
                     dplyr::select(efo_id, trait),
                   by = c("EFO_ID" = "efo_id")) %>% 
  # Rename to `TRAIT`
  dplyr::rename(TRAIT = trait) %>% 
  # Remove all traits with N < 1
  dplyr::filter(N >= 1)

raw_counts %>% 
  dplyr::arrange(dplyr::desc(N)) %>% 
  DT::datatable(.)

4.3 Hits

Set minimum number of “independent” (i.e. post-clumped) SNPs per trait

min_snps = 10
hits_path = file.path(target_dir, "pegas/fst/consol/hits.rds")
hits = readRDS(hits_path) %>%
  lapply(., function(TRAIT_ID){
    # convert to DF
    TRAIT_ID %>%
        data.frame(.) %>% 
        tibble::rownames_to_column(var = "SNP") %>% 
        dplyr::select(SNP, FST_PEGAS = Fst)
  }) %>% 
  dplyr::bind_rows(.id = "EFO_ID") %>% 
  dplyr::left_join(all_traits_tbl %>% 
                     dplyr::select(EFO_ID = efo_id,
                                   TRAIT = trait),
                   by = "EFO_ID")

# Get counts
hits_counts = hits %>% 
  dplyr::count(EFO_ID, TRAIT) %>% 
  dplyr::rename(N = n)

hits_counts %>% 
  dplyr::arrange(dplyr::desc(N)) %>% 
  DT::datatable(.)
# Get vector of EFO_IDs with more than `min_snps`
keep_snps = hits %>%
  dplyr::count(EFO_ID) %>%
  dplyr::filter(n >= min_snps) %>% 
  dplyr::pull(EFO_ID)

# How many traits pass threshold
length(keep_snps)
## [1] 587
# Filter hits for those traits
hits_filt = hits %>% 
  dplyr::filter(EFO_ID %in% keep_snps)

4.4 Controls

NOTE one SNP ID can map to multiple loci, so there are duplicate SNP IDs with different Fst measures.

In the code below we remove all the duplicated SNPs which have periods in their names, e.g. {SNP_ID}.1 or {SNP_ID}.2.

That is, we keep the first SNP in the genome with the ID. This would bias toward SNPs “earlier” in the genome. Probably not consequential?

# Fst controls
controls_path = file.path(target_dir, "pegas/fst/consol/controls.rds")
controls = readRDS(controls_path) %>%
  lapply(., function(TRAIT_ID){
    # convert to DF
    TRAIT_ID %>%
        data.frame(.) %>% 
        tibble::rownames_to_column(var = "SNP") %>% 
        dplyr::select(SNP, FST_PEGAS = Fst)
  }) %>% 
  dplyr::bind_rows(.id = "EFO_ID") %>% 
  dplyr::left_join(all_traits_tbl %>% 
                     dplyr::select(EFO_ID = efo_id,
                                   TRAIT = trait),
                   by = "EFO_ID") %>% 
  # NOTE: remove duplicated IDs
  dplyr::filter(!grepl("\\.", SNP))

# Filter controls for traits with more than `min_snps` post-clumping
controls_filt = controls %>% 
  dplyr::filter(EFO_ID %in% keep_snps)

4.6 Add palette based on pre-clump SNP count

pal_all = turbo(n = nrow(raw_counts))
names(pal_all) = raw_counts %>% 
  dplyr::arrange(desc(N)) %>% 
  dplyr::pull(TRAIT)

5 SNP counts pre- and post-clumping

snp_count_plot = list("PRE-CLUMP" = raw_counts,
                      "POST-CLUMP" = hits_counts) %>% 
  dplyr::bind_rows(.id = "FILTER") %>% 
  # order variables
  dplyr::mutate(TRAIT = factor(TRAIT, levels = names(pal_all)),
                FILTER = factor(FILTER, levels = c("PRE-CLUMP", "POST-CLUMP"))) %>% 
  ggplot() +
    geom_col(aes(TRAIT, N, fill = TRAIT)) +
    scale_fill_manual(values = pal_all) +
    theme_bw() +
    theme(axis.text.x = element_blank(),
          axis.title.x = element_blank(),
          axis.ticks.x = element_blank()) +
    ylab("N LOCI") +
    facet_wrap(~FILTER, nrow = 2) +
    guides(fill = "none")

plotly::ggplotly(snp_count_plot,
                 tooltip = c("TRAIT"))
width = 20
height = 10

ggsave(here::here("docs/plots/20210901_snp_counts.png"),
       plot = snp_count_plot,
       device = "png",
       width = width,
       height = height,
       units = "in",
       dpi= 400)

ggsave(here::here("docs/plots/20210901_snp_counts.svg"),
       plot = snp_count_plot,
       device = "svg",
       width = width,
       height = height,
       units = "in")

6 eCDF for \(F_{ST}\)

6.1 Traits ordered by median Fst, with number of loci

# Using `hits_filt` -- the traits with >= 5 SNPs
hits_filt %>% 
  dplyr::group_by(TRAIT) %>% 
  dplyr::summarise(MEDIAN_FST = median(FST_PEGAS, na.rm = T),
                   N_LOCI = dplyr::n()) %>% 
  dplyr::arrange(dplyr::desc(MEDIAN_FST)) %>% 
  DT::datatable(.)

6.2 New palette for clumped traits

# set number of colours in gradient
n_cols = 100

6.2.1 Based on rank

# by median
rank_fst_50 = hits_filt %>% 
  dplyr::group_by(TRAIT) %>% 
  dplyr::summarise(MEDIAN_FST = median(FST_PEGAS, na.rm = T)) %>% 
  dplyr::arrange(MEDIAN_FST) %>% 
  dplyr::pull(TRAIT)

rank_pal_50 = turbo(n = length(rank_fst_50))
names(rank_pal_50) = rank_fst_50


# By 90 percentile
rank_fst_90 = hits_filt %>% 
  dplyr::group_by(TRAIT) %>% 
  dplyr::summarise(MEDIAN_FST = quantile(FST_PEGAS, probs = 0.9, na.rm = T)) %>% 
  dplyr::arrange(MEDIAN_FST) %>% 
  dplyr::pull(TRAIT)

rank_pal_90 = turbo(n = length(rank_fst_90))
names(rank_pal_90) = rank_fst_90

# Look at scale by rank
scales::show_col(rank_pal_50, labels = F)

6.2.2 Based on Median Fst on continous scale

# create new palette based on median Fst
median_fst = hits_filt %>% 
  dplyr::group_by(TRAIT) %>% 
  dplyr::summarise(MEDIAN_FST = median(FST_PEGAS, na.rm = T)) %>% 
  dplyr::arrange(MEDIAN_FST) %>%
  # add negative log
  dplyr::mutate(MEDIAN_FST_ADJ = ifelse(MEDIAN_FST <= 0, NA, MEDIAN_FST),
                MEDIAN_FST_NL = -log(MEDIAN_FST_ADJ))

# Get min and max to build palette
bins = seq(min(median_fst$MEDIAN_FST),
           max(median_fst$MEDIAN_FST),
           length.out = n_cols + 1)

log_bins = seq(min(median_fst$MEDIAN_FST_NL, na.rm = T),
               max(median_fst$MEDIAN_FST_NL, na.rm = T),
               length.out = n_cols + 1)

# Create tibble of palette
ord_pal_df = tibble::tibble(COLOUR = turbo(n = n_cols),
                            COLOUR_LOG = turbo(n = n_cols),
                            COLOUR_BIN = seq(1, n_cols),
                            COLOUR_BIN_LOG = rev(seq(1, n_cols)))


# Add `COLOUR_BIN` and `COLOUR`
median_fst = median_fst %>% 
  dplyr::mutate(COLOUR_BIN = cut(MEDIAN_FST, breaks = bins, labels = F, include.lowest = T),
                COLOUR_BIN_LOG = cut(MEDIAN_FST_NL, breaks = log_bins, labels = F, include.lowest = T)) %>% 
  dplyr::left_join(ord_pal_df %>% 
                     dplyr::select(COLOUR, COLOUR_BIN),
                   by = "COLOUR_BIN") %>% 
  dplyr::left_join(ord_pal_df %>% 
                     dplyr::select(COLOUR_LOG, COLOUR_BIN_LOG),
                   by = "COLOUR_BIN_LOG")

ord_pal = median_fst$COLOUR
names(ord_pal) = median_fst$TRAIT

scales::show_col(ord_pal, labels = F)

ord_pal_log = median_fst$COLOUR_LOG
names(ord_pal_log) = median_fst$TRAIT

scales::show_col(ord_pal_log, labels = F)

Just use rank?

6.3 Plot all

ecdf_all_faceted = hits_filt %>% 
  # order
  dplyr::mutate(TRAIT = factor(TRAIT, levels = names(rank_pal_50))) %>% 
  ggplot() +
    stat_ecdf(aes(FST_PEGAS, colour = TRAIT)) +
    scale_colour_manual(values = rank_pal_50) +
    theme_bw() +
    ggtitle("eCDF") +
    xlab(expression(italic(F[ST]))) +
    facet_wrap(~TRAIT) +
    guides(colour = "none") +
    theme(strip.text.x = element_text(size = 4.5))

ggsave(here::here("docs/plots/20210901_ecdf_all_faceted.png"),
       plot = ecdf_all_faceted, 
       device = "png",
       width = 30,
       height = 22,
       units = "in",
       dpi = 400)
knitr::include_graphics(here::here("docs/plots/20210901_ecdf_all_faceted.png"))

6.4 Plotly

6.4.1 All

plotly_ecdf_all = hits_filt %>% 
  # order FST_PEGAS so that it's properly plotted by plotly
  dplyr::arrange(EFO_ID, FST_PEGAS) %>% 
  # order
  dplyr::mutate(TRAIT = factor(TRAIT, levels = names(rank_pal_50))) %>% 
  ggplot() +
    stat_ecdf(aes(FST_PEGAS, colour = TRAIT)) +
    scale_colour_manual(values = rank_pal_50) +
    theme_bw() +
    ggtitle("eCDF") +
    guides(colour = "none") +
    theme(strip.text.x = element_text(size = 5))

plotly::ggplotly(plotly_ecdf_all,
                 tooltip = c("TRAIT"))
## Warning: Removed 5 rows containing non-finite values (stat_ecdf).

6.4.2 Select

Select interesting traits

# get traits to look closer at
target_traits = c("total cholesterol measurement",
                  "late-onset Alzheimers disease",
                  "body height",
                  "body mass index",
                  "mathematical ability",
                  "intelligence",
                  "self reported educational attainment",
                  "type ii diabetes mellitus",
                  "asthma",
                  "HIV-1 infection",
                  "cognitive function measurement", 
                  "heart failure",
                  "diabetes mellitus",
                  "coronary artery disease",
                  "mortality",
                  "longevity",
                  "schizophrenia",
                  "skin pigmentation",
                  "skin pigmentation measurement",
                  "eye colour measurement",
                  "facial morphology measurement",
                  "squamous cell carcinoma",
                  "tuberculosis",
                  "cleft palate",
                  "neurotic disorder",
                  "reaction time measurement",
                  "endometrial carcinoma",
                  "BMI-adjusted hip circumference",
                  "alcohol dependence measurement",
                  "loneliness measurement",
                  "COVID-19"
                  )



plotly_ecdf_select = hits_filt %>%
  # filter for `target_traits`
  dplyr::filter(TRAIT %in% target_traits) %>%
  # add `MEDIAN_FST` %>% 
  dplyr::left_join(median_fst,
                   by = "TRAIT") %>% 
  # order FST_PEGAS so that it's properly plotted by plotly
  dplyr::arrange(EFO_ID, FST_PEGAS) %>% 
  # order
  dplyr::mutate(TRAIT = factor(TRAIT, levels = names(rank_pal_50))) %>% 
  ggplot(aes(FST_PEGAS, colour = TRAIT)) +
    stat_ecdf() +
    #scale_colour_gradientn(colours = turbo(100)) +
    scale_colour_manual(values = rank_pal_50) +
    theme_bw() +
    ggtitle("eCDF") +
    guides(colour = "none")

plotly::ggplotly(plotly_ecdf_select,
                 tooltip = c("TRAIT"))
target_traits_sml = c("lipoprotein A measurement",
                      "Alzheimer's disease",
                      "total cholesterol measurement",
                      "body mass index",
                      "alcohol drinking",
                      "body height",
                      "type ii diabetes mellitus",
                      "balding measurement",
                      "self reported educational attainment",
                      "mathematical ability",
                      "intelligence",
                      "schizophrenia",
                      "cognitive function measurement", 
                      "skin pigmentation",
                      "BMI-adjusted hip circumference",
                      "hair shape measurement",
                      "HIV-1 infection",
                      "viral load",
                      "skin pigmentation measurement",
                      "eye colour measurement")

plotly_ecdf_sml = hits_filt %>%
  # filter for `target_traits`
  dplyr::filter(TRAIT %in% target_traits_sml) %>%
  # take unique SNPs
  dplyr::group_by(EFO_ID) %>%
  dplyr::distinct(SNP, .keep_all = T) %>% 
  dplyr::ungroup() %>% 
  # order FST_PEGAS so that it's properly plotted by plotly
  dplyr::arrange(EFO_ID, FST_PEGAS) %>% 
  # order
  dplyr::mutate(TRAIT = factor(TRAIT, levels = names(rank_pal_50))) %>% 
  ggplot() +
    stat_ecdf(aes(FST_PEGAS, colour = TRAIT)) +
    scale_colour_manual(values = rank_pal_50) +
    theme_bw() +
    ggtitle("eCDF") +
    guides(colour = "none") +
    theme(strip.text.x = element_text(size = 5))

plotly::ggplotly(plotly_ecdf_sml,
                 tooltip = c("TRAIT"))
## Warning: Removed 1 rows containing non-finite values (stat_ecdf).

7 Extract same number of top SNPs for selected traits as skin pigmentation

To check whether the distribution changes if you only have a few SNPs with presumably the highest effect sizes.

# How many unique SNPs for each trait
hits_filt %>% 
  dplyr::filter(TRAIT %in% target_traits_sml) %>% 
  dplyr::group_by(TRAIT) %>% 
  dplyr::summarise(SNP_COUNT = n_distinct(SNP)) %>% 
  DT::datatable(.)
# Get highest number of SNPs of the pigmentation traits
max_n_snps = hits_filt %>% 
  dplyr::filter(TRAIT %in% target_traits_sml) %>% 
  dplyr::group_by(TRAIT) %>% 
  dplyr::summarise(SNP_COUNT = n_distinct(SNP)) %>% 
  dplyr::filter(TRAIT %in% c("skin pigmentation measurement", "skin pigmentation", "eye colour measurement")) %>% 
  dplyr::pull(SNP_COUNT) %>% 
  max(.)

# Get EFO IDs for target traits
target_efo_ids = hits_filt %>% 
  dplyr::filter(TRAIT %in% target_traits_sml) %>% 
  dplyr::distinct(EFO_ID) %>% 
  dplyr::pull(EFO_ID)

# Filter `clumped` for `target_traits_sml` and then pull out the top `max_n_snps` for each trait
clumped_red = clumped[names(clumped) %in% target_efo_ids] %>% 
  dplyr::bind_rows(.id = "EFO_ID") %>% 
  dplyr::arrange(EFO_ID, P) %>% 
  dplyr::group_by(EFO_ID) %>%
  dplyr::distinct(SNP, .keep_all = T) %>% 
  dplyr::slice_head(n = max_n_snps) %>% 
  split(., f = .$EFO_ID)

# Filter `fst_all` for clumped SNPs
## Split by `EFO_ID`
fst_all = hits_filt %>% 
  split(., f = .$EFO_ID)
## Filter for `target_efo_ids`
fst_clumped_red = fst_all[names(fst_all) %in% target_efo_ids]
## Get vector of efo_ids
clumped_red_names = names(fst_clumped_red)
## For each efo_id, take the top `max_n_snps`
fst_clumped_red = lapply(names(fst_clumped_red), function(TRAIT_ID){
    fst_all[[TRAIT_ID]] %>% 
      dplyr::filter(SNP %in% clumped_red[[TRAIT_ID]]$SNP)
})
names(fst_clumped_red) = clumped_red_names
# Bind into DF
fst_clumped_df_red = fst_clumped_red %>% 
  dplyr::bind_rows(.id = "EFO_ID") 

# Plot
plotly_ecdf_select_2 = fst_clumped_df_red %>% 
  # order FST_PEGAS so that it's properly plotted by plotly
  dplyr::arrange(EFO_ID, FST_PEGAS) %>% 
  # order
  dplyr::mutate(TRAIT = factor(TRAIT, levels = names(rank_pal_50))) %>% 
  ggplot() +
    stat_ecdf(aes(FST_PEGAS, colour = TRAIT)) +
    scale_colour_manual(values = rank_pal_50) +
    theme_bw() +
    ggtitle("eCDF") +
    guides(colour = "none") +
    theme(strip.text.x = element_text(size = 5))

plotly::ggplotly(plotly_ecdf_select_2,
                 tooltip = c("TRAIT"))

8 Were the HIV and viral load studies carried out in African populations?

# Get studies
hiv_traits = c("viral load", "HIV-1 infection")
hiv_efo_ids = all_traits_tbl %>% 
  dplyr::filter(trait %in% hiv_traits)
assocs_dir = "/nfs/research/birney/users/ian/hmn_fst/gwasrapidd/20210809/associations_raw"
hiv_studies = lapply(hiv_efo_ids$efo_id, function(HIV_EFO){
  out = list()
  # read in associations
  out[["ASSOCS"]] = readRDS(file.path(assocs_dir, paste(HIV_EFO, ".rds", sep = "")))
  out[["KEY"]] = gwasrapidd::association_to_study(unique(out[["ASSOCS"]]@associations$association_id))
  out[["STUDIES"]] = gwasrapidd::get_studies(study_id = unique(out[["KEY"]]$study_id))
  return(out)
})
names(hiv_studies) = hiv_efo_ids$trait

# Print studies
hiv_out = purrr::map(hiv_studies, function(HIV_TRAIT){
  HIV_TRAIT[["STUDIES"]]@studies %>% 
    dplyr::distinct(study_id,
                    initial_sample_size,
                    replication_sample_size) %>% 
    dplyr::left_join(HIV_TRAIT[["STUDIES"]]@publications %>% 
                       dplyr::select(study_id, title),
                     by = "study_id") %>% 
    dplyr::select(study_id, title, initial_sample_size, replication_sample_size)
})
 
# HIV-1 infection
DT::datatable(hiv_out$`HIV-1 infection`)
# Viral load
DT::datatable(hiv_out$`viral load`)

9 Final figures

9.1 Ridges

ridges_plot = hits_filt %>% 
  # filter for `target_traits`
  dplyr::filter(TRAIT %in% target_traits_sml) %>% 
  # group by trait to take unique SNPs
  dplyr::group_by(TRAIT) %>% 
  dplyr::distinct(SNP, .keep_all = T) %>% 
  dplyr::ungroup() %>% 
  # reverse order of traits to put `body height` at the top
  dplyr::mutate(TRAIT_REV = factor(TRAIT, levels = rev(names(rank_pal_50)))) %>% 
  # plot
  ggplot(aes(FST_PEGAS, TRAIT_REV, fill = TRAIT_REV, colour = TRAIT_REV)) +
    geom_density_ridges(scale = 2,
                        bandwidth = 0.003,
                        calc_ecdf = TRUE,
                        quantiles = 0.5,
                        quantile_lines = T,
                        jittered_points = T,
                        point_shape = '|', alpha = 0.85, point_size = 2,
                        position = position_points_jitter(height = 0),
                        ) +
    scale_fill_manual(values = rank_pal_50) +
    scale_colour_manual(values = darker(rank_pal_50)) +
    guides(fill = "none", colour = "none") +
    theme_cowplot() +
    scale_y_discrete(expand = expansion(add = c(0.2, 2.3))) +
    xlab(expression(italic(F[ST]))) +
    ylab(NULL)

ridges_plot
## Warning: Removed 1 rows containing non-finite values (stat_density_ridges).

width = 8
height = 20

ggsave(here::here("docs/plots/20210901_ridges.png"),
       device = "png",
       width = width,
       height = height,
       units = "in",
       dpi= 400)

ggsave(here::here("docs/plots/20210901_ridges.svg"),
       device = "svg",
       width = width,
       height = height,
       units = "in")

9.2 eCDF plot faceted

9.2.1 Get control data

hits_v_controls = list("HIT" = hits_filt %>% 
                         dplyr::filter(TRAIT %in% target_traits_sml),
                       "CONTROL" = controls_filt %>% 
                         dplyr::filter(TRAIT %in% target_traits_sml)) %>% 
  dplyr::bind_rows(.id = "TYPE") %>% 
  # create column for palette
  dplyr::mutate(PAL_TRAIT = dplyr::if_else(TYPE == "HIT",
                                           TRAIT,
                                           "control"))

# Add colour to palette
rank_pal_50_new = c("control" = "#2E3138", rank_pal_50)

9.2.2 Plot

ecdf_plot_face = hits_v_controls %>%
  # filter for `target_traits`
  dplyr::filter(TRAIT %in% target_traits_sml) %>%
  # take unique SNPs
  dplyr::group_by(EFO_ID) %>%
  dplyr::distinct(SNP, .keep_all = T) %>% 
  dplyr::ungroup() %>% 
  # order FST_PEGAS so that it's properly plotted by plotly
  dplyr::arrange(EFO_ID, FST_PEGAS) %>% 
  # order
  dplyr::mutate(TRAIT = factor(TRAIT, levels = names(rank_pal_50)),
                PAL_TRAIT = factor(PAL_TRAIT, levels = names(rank_pal_50_new))) %>% 
  ggplot() +
    stat_ecdf(aes(FST_PEGAS, colour = PAL_TRAIT)) +
    scale_colour_manual(values = rank_pal_50_new) +
    theme_cowplot(rel_small = 9/14) +
    guides(colour = "none") +
    theme(strip.text.x = element_text(size = 5)) +
    xlab(expression(italic(F[ST]))) +
    ylab("Cumulative Probability") +
    facet_wrap(~TRAIT, nrow = 5, ncol = 4) 

ecdf_plot_face
## Warning: Removed 21 rows containing non-finite values (stat_ecdf).

9.2.3 Calculate KS test comparing between the distributions of hits and controls

# Split into list by trait
ks_list = hits_v_controls %>% 
  split(., f = .$TRAIT) %>% 
  purrr::map(., function(TRAIT){
    split(TRAIT, f = TRAIT$TYPE)
  })

# `Run ks.test`
ks_out = purrr::map(ks_list, function(TRAIT){
  ks.test(TRAIT$HIT$FST_PEGAS,
          TRAIT$CONTROL$FST_PEGAS,
          alternative = "two.sided")
})
## Warning in ks.test(TRAIT$HIT$FST_PEGAS, TRAIT$CONTROL$FST_PEGAS, alternative = "two.sided"): p-value will be approximate in the presence of
## ties

## Warning in ks.test(TRAIT$HIT$FST_PEGAS, TRAIT$CONTROL$FST_PEGAS, alternative = "two.sided"): p-value will be approximate in the presence of
## ties

## Warning in ks.test(TRAIT$HIT$FST_PEGAS, TRAIT$CONTROL$FST_PEGAS, alternative = "two.sided"): p-value will be approximate in the presence of
## ties

## Warning in ks.test(TRAIT$HIT$FST_PEGAS, TRAIT$CONTROL$FST_PEGAS, alternative = "two.sided"): p-value will be approximate in the presence of
## ties

## Warning in ks.test(TRAIT$HIT$FST_PEGAS, TRAIT$CONTROL$FST_PEGAS, alternative = "two.sided"): p-value will be approximate in the presence of
## ties
## Warning in ks.test(TRAIT$HIT$FST_PEGAS, TRAIT$CONTROL$FST_PEGAS, alternative = "two.sided"): cannot compute exact p-value with ties
## Warning in ks.test(TRAIT$HIT$FST_PEGAS, TRAIT$CONTROL$FST_PEGAS, alternative = "two.sided"): p-value will be approximate in the presence of
## ties

## Warning in ks.test(TRAIT$HIT$FST_PEGAS, TRAIT$CONTROL$FST_PEGAS, alternative = "two.sided"): p-value will be approximate in the presence of
## ties

## Warning in ks.test(TRAIT$HIT$FST_PEGAS, TRAIT$CONTROL$FST_PEGAS, alternative = "two.sided"): p-value will be approximate in the presence of
## ties

## Warning in ks.test(TRAIT$HIT$FST_PEGAS, TRAIT$CONTROL$FST_PEGAS, alternative = "two.sided"): p-value will be approximate in the presence of
## ties

## Warning in ks.test(TRAIT$HIT$FST_PEGAS, TRAIT$CONTROL$FST_PEGAS, alternative = "two.sided"): p-value will be approximate in the presence of
## ties

## Warning in ks.test(TRAIT$HIT$FST_PEGAS, TRAIT$CONTROL$FST_PEGAS, alternative = "two.sided"): p-value will be approximate in the presence of
## ties
## Warning in ks.test(TRAIT$HIT$FST_PEGAS, TRAIT$CONTROL$FST_PEGAS, alternative = "two.sided"): cannot compute exact p-value with ties
# Pull out statistic
ks_stats = purrr::map_dfr(ks_out, function(TRAIT){
  TRAIT$statistic
}, .id = "TRAIT")

# Plot
ks_plot = ks_stats %>% 
  dplyr::mutate(TRAIT = factor(TRAIT, levels = names(rank_pal_50))) %>% 
  ggplot() +
    geom_col(aes(TRAIT, D, fill = TRAIT)) +
    scale_fill_manual(values = rank_pal_50) +
    theme_cowplot() +
    theme(axis.title.x=element_blank(),
          axis.text.x=element_blank(),
          axis.ticks.x=element_blank()) +
    guides(fill = "none") 

ggplotly(ks_plot, tooltip = c("x", "y"))

D statistic from KS test comparing distributions of hits vs controls 

ecdf_plot_all = hits_filt %>%
  # filter for `target_traits`
  dplyr::filter(TRAIT %in% target_traits_sml) %>%
  # take unique SNPs
  dplyr::group_by(EFO_ID) %>%
  dplyr::distinct(SNP, .keep_all = T) %>% 
  dplyr::ungroup() %>% 
  # order FST_PEGAS so that it's properly plotted by plotly
  dplyr::arrange(EFO_ID, FST_PEGAS) %>% 
  # order
  dplyr::mutate(TRAIT = factor(TRAIT, levels = names(rank_pal_50))) %>% 
  ggplot() +
    stat_ecdf(aes(FST_PEGAS, colour = TRAIT)) +
    scale_colour_manual(values = rank_pal_50) +
    theme_cowplot() +
    guides(colour = "none") +
    theme(strip.text.x = element_text(size = 5)) +
    xlab(expression(italic(F[ST]))) +
    ylab("Cumulative Probability")

ecdf_plot_all
## Warning: Removed 1 rows containing non-finite values (stat_ecdf).

9.3 Compile

final_figure = cowplot::ggdraw() +
  draw_plot(ridges_plot,
            x = 0, y = 0, width = .5, height = 1) +
  draw_plot(ecdf_plot_face,
            x = .5, y = 0.35, width = .5, height = .65) +
  draw_plot(ecdf_plot_all,
            x = .5, y = 0, width = .5, height = .35) +
  draw_plot_label(label = c("A", "B", "C"), size = 25,
                  x = c(0, .5, .5), y = c(1, 1, .35),
                  hjust = c(-.15, .15, .15),
                  color = "#37323E")
## Warning: Removed 1 rows containing non-finite values (stat_density_ridges).
## Warning: Removed 21 rows containing non-finite values (stat_ecdf).
## Warning: Removed 1 rows containing non-finite values (stat_ecdf).
width = 12
height = 12

ggsave(here::here("docs/plots/20210904_final.png"),
       final_figure,
       device = "png",
       width = width,
       height = height,
       units = "in",
       dpi= 400)

ggsave(here::here("docs/plots/20210904_final.svg"),
       final_figure,
       device = "svg",
       width = width,
       height = height,
       units = "in")
knitr::include_graphics(here::here("docs/plots/20210904_final.png"))